The automatic segmentation of hair in images is required for a variety of applications, including simulation of hair color on user uploaded images, automatic layer-based correction and adjustment of images, and the extraction of hairstyles in images. A variety of methods have been proposed in the past, including mating learning methods [1,2], region fitting [3], active contours [4,5], video based methods [6,7], as well as other methods [8,9]. A listing of references is set forth below, each of which is incorporated herein by reference. The majority of the existing methods rely on either color, or texture, or both, as a means of determining a segmentation mask for the hair.
Depending on the application, different levels of accuracy and speed are required for hair segmentation. For example, the extraction of hair for subsequent use as a virtual hairstyle requires the segmentation of even the minutest details of the hair, including individual hair strands. This, however, can be accomplished offline without strict timing limitations. On the other hand, the simulation of hair color on either a still image or a live video feed requires computationally efficient segmentations but can tolerate the possible reduction in hair detection accuracy.